Antiproliferative Effect of Lapatinib in HER2-Positive and HER2-Negative/HER3-High Breast Cancer: Results of the Presurgical Randomized MAPLE Trial (CRUK E/06/039)

Exploration and validation of key genes associated with early lymph node metastasis in thyroid carcinoma using weighted gene co-expression network analysis and machine learning

Background

Thyroid carcinoma (THCA), the most common endocrine neoplasm, typically exhibits an indolent behavior. However, in some instances, lymph node metastasis (LNM) may occur in the early stages, with the underlying mechanisms not yet fully understood.

Materials and methods

LNM potential was defined as the tumor's capability to metastasize to lymph nodes at an early stage, even when the tumor volume is small. We performed differential expression analysis using the 'Limma' R package and conducted enrichment analyses using the Metascape tool. Co-expression networks were established using the 'WGCNA' R package, with the soft threshold power determined by the 'pickSoftThreshold' algorithm. For unsupervised clustering, we utilized the 'ConsensusCluster Plus' R package. To determine the topological features and degree centralities of each node (protein) within the Protein-Protein Interaction (PPI) network, we used the CytoNCA plugin integrated with the Cytoscape tool. Immune cell infiltration was assessed using the Immune Cell Abundance Identifier (ImmuCellAI) database. We applied the Least Absolute Shrinkage and Selection Operator (LASSO), Support Vector Machine (SVM), and Random Forest (RF) algorithms individually, with the 'glmnet,' 'e1071,' and 'randomForest' R packages, respectively. Ridge regression was performed using the 'oncoPredict' algorithm, and all the predictions were based on data from the Genomics of Drug Sensitivity in Cancer (GDSC) database. To ascertain the protein expression levels and subcellular localization of genes, we consulted the Human Protein Atlas (HPA) database. Molecular docking was carried out using the mcule 1-click Docking server online. Experimental validation of gene and protein expression levels was conducted through Real-Time Quantitative PCR (RT-qPCR) and immunohistochemistry (IHC) assays.

Results

Through WGCNA and PPI network analysis, we identified twelve hub genes as the most relevant to LNM potential from these two modules. These 12 hub genes displayed differential expression in THCA and exhibited significant correlations with the downregulation of neutrophil infiltration, as well as the upregulation of dendritic cell and macrophage infiltration, along with activation of the EMT pathway in THCA. We propose a novel molecular classification approach and provide an online web-based nomogram for evaluating the LNM potential of THCA (http://www.empowerstats.net/pmodel/?m=17617_LNM). Machine learning algorithms have identified ERBB3 as the most critical gene associated with LNM potential in THCA. ERBB3 exhibits high expression in patients with THCA who have experienced LNM or have advanced-stage disease. The differential methylation levels partially explain this differential expression of ERBB3. ROC analysis has identified ERBB3 as a diagnostic marker for THCA (AUC=0.89), THCA with high LNM potential (AUC=0.75), and lymph nodes with tumor metastasis (AUC=0.86). We have presented a comprehensive review of endocrine disruptor chemical (EDC) exposures, environmental toxins, and pharmacological agents that may potentially impact LNM potential. Molecular docking revealed a docking score of -10.1 kcal/mol for Lapatinib and ERBB3, indicating a strong binding affinity.

Conclusion

In conclusion, our study, utilizing bioinformatics analysis techniques, identified gene modules and hub genes influencing LNM potential in THCA patients. ERBB3 was identified as a key gene with therapeutic implications. We have also developed a novel molecular classification approach and a user-friendly web-based nomogram tool for assessing LNM potential. These findings pave the way for investigations into the mechanisms underlying differences in LNM potential and provide guidance for personalized clinical treatment plans.

The Evolution of Ki-67 and Breast Carcinoma: Past Observations, Present Directions, and Future Considerations

The 1983 discovery of a mouse monoclonal antibody-the Ki-67 antibody-that recognized a nuclear antigen present only in proliferating cells represented a seminal discovery for the pathologic assessment of cellular proliferation in breast cancer and other solid tumors. Cellular proliferation is a central determinant of prognosis and response to cytotoxic chemotherapy in patients with breast cancer, and since the discovery of the Ki-67 antibody, Ki-67 has evolved as an important biomarker with both prognostic and predictive potential in breast cancer. Although there is universal recognition among the international guideline recommendations of the value of Ki-67 in breast cancer, recommendations for the actual use of Ki-67 assays in the prognostic and predictive evaluation of breast cancer remain mixed, primarily due to the lack of assay standardization and inconsistent inter-observer and inter-laboratory reproducibility. The treatment of high-risk ER-positive/human epidermal growth factor receptor-2 (HER2) negative breast cancer with the recently FDA-approved drug abemaciclib relies on a quantitative assessment of Ki-67 expression in the treatment decision algorithm. This further reinforces the urgent need for standardization of Ki-67 antibody selection and staining interpretation, which will hopefully lead to multidisciplinary consensus on the use of Ki-67 as a prognostic and predictive marker in breast cancer. The goals of this review are to highlight the historical evolution of Ki-67 in breast cancer, summarize the present literature on Ki-67 in breast cancer, and discuss the evolving literature on the use of Ki-67 as a companion diagnostic biomarker in breast cancer, with consideration for the necessary changes required across pathology practices to help increase the reliability and widespread adoption of Ki-67 as a prognostic and predictive marker for breast cancer in clinical practice.

Patient perspectives on window of opportunity clinical trials in early-stage breast cancer

PURPOSE

Window of opportunity trials (WOT) are increasingly common in oncology research. In WOT participants receive a drug between diagnosis and anti-cancer treatment, usually for the purpose of investigating that drugs effect on cancer biology. This qualitative study aimed to understand patient perspectives on WOT.

METHODS

We recruited adults diagnosed with early-stage breast cancer awaiting definitive therapy at a single-academic medical center to participate in semi-structured interviews. Thematic and content analyses were performed to identify attitudes and factors that would influence decisions about WOT participation.

RESULTS

We interviewed 25 women diagnosed with early-stage breast cancer. The most common positive attitudes toward trial participation were a desire to contribute to research and a hope for personal benefit, while the most common concerns were the potential for side effects and how they might impact fitness for planned treatment. Participants indicated family would be an important normative factor in decision-making and, during the COVID-19 pandemic, deemed the absence of family members during clinic visits a barrier to enrollment. Factors that could hinder participation included delay in standard treatment and the requirement for additional visits or procedures. Ultimately, most interviewees stated they would participate in a WOT if offered (N = 17/25).

CONCLUSION

In this qualitative study, interviewees weighed altruism and hypothetical personal benefit against the possibility of side effect from a WOT. In-person family presence during trial discussion, challenging during COVID-19, was important for many. Our results may inform trial design and communication approaches in future window of opportunity efforts.

Combined Perioperative Lapatinib and Trastuzumab in Early HER2-Positive Breast Cancer Identifies Early Responders: Randomized UK EPHOS-B Trial Long-Term Results

PURPOSE

EPHOS-B aimed to determine whether perioperative anti-HER2 therapy inhibited proliferation and/or increased apoptosis in HER2-positive breast cancer.

PATIENTS AND METHODS

This randomized phase II, two-part, multicenter trial included newly diagnosed women with HER2-positive invasive breast cancer due to undergo surgery. Patients were randomized to: part 1 (1:2:2), no treatment (control), trastuzumab or lapatinib; part 2 (1:1:2) control, trastuzumab, or lapatinib and trastuzumab combination. Treatment was given for 11 days presurgery. Coprimary endpoints were change in Ki67 and apoptosis between baseline and surgery tumor samples (biologic response: ≥30% change). Central pathology review scored residual cancer burden (RCB). Relapse-free survival (RFS) explored long-term effects.

RESULTS

Between November 2010 and September 2015, 257 patients were randomized (part 1: control 22, trastuzumab 57, lapatinib 51; part 2: control 29, trastuzumab 32, combination 66). Ki67 response was evaluable for 223 patients: in part 1 Ki67 response occurred in 29/44 (66%) lapatinib versus 18/49 (37%) trastuzumab (P = 0.007) and 1/22 (5%) control (P < 0.0001); in part 2 in 36/49 (74%) combination versus 14/31 (45%) trastuzumab (P = 0.02) and 2/28 (7%) control (P < 0.0001). No significant increase in apoptosis after 11 days was seen in treatment groups. Six patients achieved complete pathologic response (pCR, RCB0) and 13 RCB1, all but two in the combination group. After 6 years median follow-up, 28 (11%) had recurrence and 19 (7%) died. No recurrences or deaths were observed among patients who achieved a pCR. Ki67% falls ≥50% associated with fewer recurrences (P = 0.002).

CONCLUSIONS

Early response after short duration anti-HER2 dual therapy identifies cancers dependent on the HER2 pathway providing a strategy for exploring risk-adapted individualized treatment de-escalation.

HER2 Activation and Endocrine Treatment Resistance in HER2-negative Breast Cancer

The lethality of estrogen receptor alpha positive (ER+) breast cancer, which is often considered to have better prognosis than other subtypes, is defined by resistance to the standard of care endocrine treatment. Relapse and metastasis are inevitable in almost every patient whose cancer is resistant to endocrine treatment. Therefore, understanding the underlying causes of treatment resistance remains an important biological and clinical focus of research in this area. Growth factor receptor pathway activation, specifically HER2 activation, has been identified as 1 mechanism of endocrine treatment resistance across a range of experimental model systems. However, clinical trials conducted to test whether targeting HER2 benefits patients with endocrine treatment-resistant ER+ breast cancer have consistently and disappointingly shown mixed results. One reason for the failure of these clinical trials could be the complexity of crosstalk between ER, HER2, and other growth factor receptors and the fluidity of HER2 activation in these cells, which makes it challenging to identify stratifiers for this targeted intervention. In the absence of stratifiers that can be assayed at diagnosis to allow prospective tailoring of HER2 inhibition to the right patients, clinical trials will continue to disappoint. To understand stratifiers, it is important that the field invests in key understudied areas of research including characterization of the tumor secretome and receptor activation in response to endocrine treatment, and mapping the ER-HER2 growth factor network in the normal and developing mammary gland. Understanding these mechanisms further is critical to improving outcomes for the hard-to-treat endocrine treatment-resistant ER+ breast cancer cohort.

Adverse event profiles of epidermal growth factor receptor-tyrosine kinase inhibitors in cancer patients: A systematic review and meta-analysis

The efficacy of agents targeting epidermal growth factor receptor (EGFR) in patients with various cancers was well elucidated. However, the safety profile of EGFR tyrosine kinase inhibitors (EGFR-TKIs) has not been systematically investigated. This meta-analysis aimed to evaluate the safety profile of EGFR-TKIs in patients with cancer. A systematic search of PubMed, EMBASE, Cochrane Library databases, ASCO, and ESMO abstracts were conducted. Randomized controlled trials (RCTs) that compared safety profile of EGFR-TKIs with placebo were included. The end points included treatment-related adverse events (AEs), treatment discontinuation, and toxic death. Twenty-eight RCTs containing 17,800 patients were included. The analyses showed that the most frequently observed all-grade AEs in patients treated with EGFR-TKIs were diarrhea (53.7%), rash (48.6%), mucositis (46.5%), alanine aminotransferase (ALT) increased (38.9%), and skin reaction (35.2%). The most common high-grade (grade ≥3) AEs were mucositis (14.8%), pain (8.2%,), metabolism and nutrition disorders (7.4%), diarrhea (6.2%), dyspnea (6.1%), and hypertension (6.1%). The incidence of serious AEs, treatment discontinuation, and toxic death due to AEs were 18.2%, 12.36%, and 3.0%, respectively. Pooled risk ratio (RR) showed that the use of EGFR-TKIs was associated with an increased risk of developing AEs. Subgroup analysis indicated that the risk of AEs varied significantly according to tumor type, generation line, and drug type. Our meta-analysis indicates EGFR-TKIs was associated with a significant increased risk of a series of unique AEs. Early detection and proper management of AEs are important to reduce morbidity, avoid treatment discontinuation, and improve patient quality of life. Study Highlights WHAT IS THE CURRENT KNOWLEDGE ON THE TOPIC? The safety profile of epidermal growth factor receptor (EGFR)-tyrosine kinase inhibitors (TKIs) varied in different trials, and has not been systemically investigated. WHAT QUESTION DID THIS STUDY ADDRESS? We conducted this meta-analysis of randomized control trials (RCTs) to provide a comprehensive evaluation of adverse event in patients with cancer receiving EGFR-TKIs. WHAT DOES THIS STUDY ADD TO OUR KNOWLEDGE? Our meta-analysis indicates EGFR-TKIs was associated with a significant increased risk of a series of unique adverse events (AEs). HOW MIGHT THIS CHANGE CLINICAL PHARMACOLOGY OR TRANSLATIONAL SCIENCE? The integrated understanding of safety profile of EGFR-TKIs will help in the future design of new EGFR-TKIs with a better safety profile.

Assessment of Ki67 in Breast Cancer: Updated Recommendations From the International Ki67 in Breast Cancer Working Group

Ki67 immunohistochemistry (IHC), commonly used as a proliferation marker in breast cancer, has limited value for treatment decisions due to questionable analytical validity. The International Ki67 in Breast Cancer Working Group (IKWG) consensus meeting, held in October 2019, assessed the current evidence for Ki67 IHC analytical validity and clinical utility in breast cancer, including the series of scoring studies the IKWG conducted on centrally stained tissues. Consensus observations and recommendations are: 1) as for estrogen receptor and HER2 testing, preanalytical handling considerations are critical; 2) a standardized visual scoring method has been established and is recommended for adoption; 3) participation in and evaluation of quality assurance and quality control programs is recommended to maintain analytical validity; and 4) the IKWG accepted that Ki67 IHC as a prognostic marker in breast cancer has clinical validity but concluded that clinical utility is evident only for prognosis estimation in anatomically favorable estrogen receptor–positive and HER2-negative patients to identify those who do not need adjuvant chemotherapy. In this T1-2, N0-1 patient group, the IKWG consensus is that Ki67 5% or less, or 30% or more, can be used to estimate prognosis. In conclusion, analytical validity of Ki67 IHC can be reached with careful attention to preanalytical issues and calibrated standardized visual scoring. Currently, clinical utility of Ki67 IHC in breast cancer care remains limited to prognosis assessment in stage I or II breast cancer. Further development of automated scoring might help to overcome some current limitations.

Pathologic and molecular responses to neoadjuvant trastuzumab and/or lapatinib from a phase II randomized trial in HER2-positive breast cancer (TRIO-US B07)

In this multicenter, open-label, randomized phase II investigator-sponsored neoadjuvant trial with funding provided by Sanofi and GlaxoSmithKline (TRIO-US B07, Clinical Trials NCT00769470), participants with early-stage HER2-positive breast cancer (N = 128) were recruited from 13 United States oncology centers throughout the Translational Research in Oncology network. Participants were randomized to receive trastuzumab (T; N = 34), lapatinib (L; N = 36), or both (TL; N = 58) as HER2-targeted therapy, with each participant given one cycle of this designated anti-HER2 therapy alone followed by six cycles of standard combination chemotherapy with the same anti-HER2 therapy. The primary objective was to estimate the rate of pathologic complete response (pCR) at the time of surgery in each of the three arms. In the intent-to-treat population, we observed similar pCR rates between T (47%, 95% confidence interval [CI] 30-65%) and TL (52%, 95% CI 38-65%), and a lower pCR rate with L (25%, 95% CI 13-43%). In the T arm, 100% of participants completed all protocol-specified treatment prior to surgery, as compared to 69% in the L arm and 74% in the TL arm. Tumor or tumor bed tissue was collected whenever possible pre-treatment (N = 110), after one cycle of HER2-targeted therapy alone (N = 89), and at time of surgery (N = 59). Higher-level amplification of HER2 and hormone receptor (HR)-negative status were associated with a higher pCR rate. Large shifts in the tumor, immune, and stromal gene expression occurred after one cycle of HER2-targeted therapy. In contrast to pCR rates, the L-containing arms exhibited greater proliferation reduction than T at this timepoint. Immune expression signatures increased in all arms after one cycle of HER2-targeted therapy, decreasing again by the time of surgery. Our results inform approaches to early assessment of sensitivity to anti-HER2 therapy and shed light on the role of the immune microenvironment in response to HER2-targeted agents.

Molecular Subtyping of Triple-Negative Breast Cancers by Immunohistochemistry: Molecular Basis and Clinical Relevance

BACKGROUND

Molecular subtyping of triple-negative breast cancers (TNBCs) via gene expression profiling is essential for understanding the molecular essence of this heterogeneous disease and for guiding individualized treatment. We aim to devise a clinically practical method based on immunohistochemistry (IHC) for the molecular subtyping of TNBCs.

MATERIALS AND METHODS

By analyzing the RNA sequencing data on TNBCs from Fudan University Shanghai Cancer Center (FUSCC) (n = 360) and The Cancer Genome Atlas data set (n = 158), we determined markers that can identify specific molecular subtypes. We performed immunohistochemical staining on tumor sections of 210 TNBCs from FUSCC, established an IHC-based classifier, and applied it to another two cohorts (n = 183 and 214).

RESULTS

We selected androgen receptor (AR), CD8, FOXC1, and DCLK1 as immunohistochemical markers and classified TNBCs into five subtypes based on the staining results: (a) IHC-based luminal androgen receptor (IHC-LAR; AR-positive [+]), (b) IHC-based immunomodulatory (IHC-IM; AR-negative [-], CD8+), (c) IHC-based basal-like immune-suppressed (IHC-BLIS; AR-, CD8-, FOXC1+), (d) IHC-based mesenchymal (IHC-MES; AR-, CD8-, FOXC1-, DCLK1+), and (e) IHC-based unclassifiable (AR-, CD8-, FOXC1-, DCLK1-). The κ statistic indicated substantial agreement between the IHC-based classification and mRNA-based classification. Multivariate survival analysis suggested that our IHC-based classification was an independent prognostic factor for relapse-free survival. Transcriptomic data and pathological observations implied potential treatment strategies for different subtypes. The IHC-LAR subtype showed relative activation of HER2 pathway. The IHC-IM subtype tended to exhibit an immune-inflamed phenotype characterized by the infiltration of CD8+ T cells into tumor parenchyma. The IHC-BLIS subtype showed high expression of a VEGF signature. The IHC-MES subtype displayed activation of JAK/STAT3 signaling pathway.

CONCLUSION

We developed an IHC-based approach to classify TNBCs into molecular subtypes. This IHC-based classification can provide additional information for prognostic evaluation. It allows for subgrouping of TNBC patients in clinical trials and evaluating the efficacy of targeted therapies within certain subtypes.

IMPLICATIONS FOR PRACTICE

An immunohistochemistry (IHC)-based classification approach was developed for triple-negative breast cancer (TNBC), which exhibited substantial agreement with the mRNA expression-based classification. This IHC-based classification (a) allows for subgrouping of TNBC patients in large clinical trials and evaluating the efficacy of targeted therapies within certain subtypes, (b) will contribute to the practical application of subtype-specific treatment for patients with TNBC, and (c) can provide additional information beyond traditional prognostic factors in relapse prediction.

Window of Opportunity trials for biomarker discovery in breast cancer

PURPOSE OF REVIEW

Window of Opportunity (WOO) studies have gain their place in current clinical and translational research in breast cancer patients. This review provides current information and future applications of this specific type of research.

RECENT FINDINGS

So far, WOO trials in breast cancer patients have demonstrated their utility in breast cancer research as: first they allow administering a treatment for a short period of time to treatment-naïve patients whose tumors have not developed mechanisms of resistance or heterogeneity because of previous therapies. Second, it brings a unique opportunity for translational research providing easy access to tumor tissue in order to evaluate antitumor effect from initial biopsy and from surgical resection specimen. They provide the perfect scenario for biomarker discovery and validation in an efficient and timely manner and valuable information about drug pharmacodynamics. Several issues need to be contemplated when designing and performing this type of trials including choice of a biological surrogate endpoint of efficacy as standard clinical activity endpoints are not feasible.

SUMMARY

Despite some limitations like the absence of information about secondary mechanisms of resistance, WOO trials represent an important support for drug development and biomarker discovery in breast cancer patients.

Stathmin expression associates with vascular and immune responses in aggressive breast cancer subgroups

Studies indicate that stathmin expression associates with PI3K activation in breast cancer, suggesting stathmin as a marker for targetable patient subgroups. Here we assessed stathmin in relation to tumour proliferation, vascular and immune responses, BRCA1 germline status, basal-like differentiation, clinico-pathologic features, and survival. Immunohistochemical staining was performed on breast cancers from two series (cohort 1, n = 187; cohort 2, n = 198), and mass spectrometry data from 24 cases and 12 breast cancer cell lines was examined for proteomic profiles. Open databases were also explored (TCGA, METABRIC, Oslo2 Landscape cohort, Cancer Cell Line Encyclopedia). High stathmin expression associated with tumour proliferation, p53 status, basal-like differentiation, BRCA1 genotype, and high-grade histology. These patterns were confirmed using mRNA data. Stathmin mRNA further associated with tumour angiogenesis, immune responses and reduced survival. By logistic regression, stathmin protein independently predicted a BRCA1 genotype (OR 10.0, p = 0.015) among ER negative tumours. Cell line analysis (Connectivity Map) implied PI3K inhibition in tumours with high stathmin. Altogether, our findings indicate that stathmin might be involved in the regulation of tumour angiogenesis and immune responses in breast cancer, in addition to tumour proliferation. Cell data point to potential effects of PI3K inhibition in tumours with high stathmin expression.

Neoadjuvant Trials in ER+ Breast Cancer: A Tool for Acceleration of Drug Development and Discovery

Neoadjuvant therapy trials offer an excellent strategy for drug development and discovery in breast cancer, particularly in triple-negative and HER2-overexpressing subtypes, where pathologic complete response is a good surrogate of long-term patient benefit. For estrogen receptor-positive (ER+) breast cancers, however, use of this strategy has been challenging because of the lack of validated surrogates of long-term efficacy and the overall good prognosis of the majority of patients with this cancer subtype. We review below the clinical benefits of neoadjuvant endocrine therapy for ER+/HER2-negative breast cancer, its use and limitations for drug development, prioritization of adjuvant and metastatic trials, and biomarker discovery.Significance: Neoadjuvant endocrine therapy is an excellent platform for the development of investigational drugs, triaging of novel combinations, biomarker validation, and discovery of mechanisms of drug resistance. This review summarizes the clinical and investigational benefits of this approach, with a focus on how to best integrate predictive biomarkers into novel clinical trial designs. Cancer Discov; 7(6); 561-74. ©2017 AACR.

Development of a test that measures real-time HER2 signaling function in live breast cancer cell lines and primary cells

Background

Approximately 18–20% of all human breast cancers have overexpressed human epidermal growth factor receptor 2 (HER2). Standard clinical practice is to treat only overexpressed HER2 (HER2+) cancers with targeted anti-HER2 therapies. However, recent analyses of clinical trial data have found evidence that HER2-targeted therapies may benefit a sub-group of breast cancer patients with non-overexpressed HER2. This suggests that measurement of other biological factors associated with HER2 cancer, such as HER2 signaling pathway activity, should be considered as an alternative means of identifying patients eligible for HER2 therapies.

Methods

A new biosensor-based test (CELx^TM HSF) that measures HER2 signaling activity in live cells is demonstrated using a set of 19 human HER2+ and HER2– breast cancer reference cell lines and primary cell samples derived from two fresh patient tumor specimens. Pathway signaling is elucidated by use of highly specific agonists and antagonists. The test method relies upon well-established phenotypic, adhesion-related, impedance changes detected by the biosensor.

Results

The analytical sensitivity and analyte specificity of this method was demonstrated using ligands with high affinity and specificity for HER1 and HER3. The HER2-driven signaling quantified ranged 50-fold between the lowest and highest cell lines. The HER2+ cell lines were almost equally divided into high and low signaling test result groups, suggesting that little correlation exists between HER2 protein expression and HER2 signaling level. Unexpectedly, the highest HER2-driven signaling level recorded was with a HER2– cell line.

Conclusions

Measurement of HER2 signaling activity in the tumor cells of breast cancer patients is a feasible approach to explore as a biomarker to identify HER2-driven cancers not currently diagnosable with genomic techniques. The wide range of HER2-driven signaling levels measured suggests it may be possible to make a distinction between normal and abnormal levels of activity. Analytical validation studies and clinical trials treating HER2- patients with abnormal HER2-driven signaling would be required to evaluate the analytical and clinical validity of using this functional biomarker as a diagnostic test to select patients for treatment with HER2 targeted therapy. In clinical practice, this method would require patient specimens be delivered to and tested in a central lab.

Electronic supplementary material

The online version of this article (doi:10.1186/s12885-017-3181-0) contains supplementary material, which is available to authorized users.

Lapatinib inhibits CIP2A/PP2A/p-Akt signaling and induces apoptosis in triple negative breast cancer cells

We tested the efficacy of lapatinib, a dual tyrosine kinase inhibitor which interrupts the HER2 and epidermal growth factor receptor (EGFR) pathways, in a panel of triple-negative breast cancer (TNBC) cells, and examined the drug mechanism. Lapatinib showed an anti-proliferative effect in HCC 1937, MDA-MB-468, and MDA-MB-231 cell lines. Lapatinib induced significant apoptosis and inhibited CIP2A and p-Akt in a dose and time-dependent manner in the three TNBC cell lines. Overexpression of CIP2A reduced lapatinib-induced apoptosis in MDA-MB-468 cells. In addition, lapatinib increased PP2A activity (in relation to CIP2A inhibition). Moreover, lapatinib-induced apoptosis and p-Akt downregulation was attenuated by PP2A antagonist okadaic acid. Furthermore, lapatinib indirectly decreased CIP2A transcription by disturbing the binding of Elk1 to the CIP2A promoter. Importantly, lapatinib showed anti-tumor activity in mice bearing MDA-MB-468 xenograft tumors, and suppressed CIP2A as well as p-Akt in these xenografted tumors. In summary, inhibition of CIP2A determines the effects of lapatinib-induced apoptosis in TNBC cells. In addition to being a dual tyrosine kinase inhibitor of HER2 and EGFR, lapatinib also inhibits CIP2A/PP2A/p-Akt signaling in TNBC cells.

BGRMI: A method for inferring gene regulatory networks from time-course gene expression data and its application in breast cancer research

Reconstructing gene regulatory networks (GRNs) from gene expression data is a challenging problem. Existing GRN reconstruction algorithms can be broadly divided into model-free and model–based methods. Typically, model-free methods have high accuracy but are computation intensive whereas model-based methods are fast but less accurate. We propose Bayesian Gene Regulation Model Inference (BGRMI), a model-based method for inferring GRNs from time-course gene expression data. BGRMI uses a Bayesian framework to calculate the probability of different models of GRNs and a heuristic search strategy to scan the model space efficiently. Using benchmark datasets, we show that BGRMI has higher/comparable accuracy at a fraction of the computational cost of competing algorithms. Additionally, it can incorporate prior knowledge of potential gene regulation mechanisms and TF hetero-dimerization processes in the GRN reconstruction process. We incorporated existing ChIP-seq data and known protein interactions between TFs in BGRMI as sources of prior knowledge to reconstruct transcription regulatory networks of proliferating and differentiating breast cancer (BC) cells from time-course gene expression data. The reconstructed networks revealed key driver genes of proliferation and differentiation in BC cells. Some of these genes were not previously studied in the context of BC, but may have clinical relevance in BC treatment.

Response biomarkers: re-envisioning the approach to tailoring drug therapy for cancer

BACKGROUND

The rapidly expanding arsenal of chemotherapeutic agents approved in the past 5 years represents significant progress in the field. However, this poses a challenge for oncologists to choose which drug or combination of drugs is best for any individual. Because only a fraction of patients respond to any drug, efforts have been made to devise strategies to personalize care. The majority of efforts have involved development of predictive biomarkers. While there are notable successes, there are no predictive biomarkers for most drugs. Moreover, predictive biomarkers enrich the cohort of individuals likely to benefit; they do not guarantee benefit.

MAIN TEXT

There is a need to devise alternate strategies to tailor cancer care. One alternative approach is to enhance the current adaptive approach, which involves administration of a drug and cessation of treatment once progression is documented. This currently involves radiographic tests for the most part, which are expensive, inconvenient and imperfect in their ability to categorize patients who are and are not benefiting from treatment. A biomarker approach to categorizing response may have advantages.

CONCLUSION

Herein, we discuss the state of the art on treatment response assessment. While the most mature technologies for response assessment involve radiographic tests such as CT and PET, reports are emerging on biomarkers used to monitor therapeutic efficacy. Potentially, response biomarkers represent a less expensive and more convenient means of monitoring therapy, although an ideal response biomarker has not yet been described. A framework for future response biomarker discovery is described.

Presurgical window of opportunity trial design as a platform for testing anticancer drugs: Pros, cons and a focus on breast cancer

The high attrition rate is a major issue in anticancer drug development. Among the alternative trial designs, presurgical window of opportunity trials envision a short course treatment in the time window between diagnostic biopsy and surgery in a moderately-sized patient population. This approach allows testing therapeutics when pre- and post-treatment tumor tissues are available for comprehensive molecular analyses. The emerging evidence may help define the ability of a given agent to modulate its target(s) and help obtain a broader picture of the molecular changes operated by the treatment. The resulting gain may outweigh the potential harms for patients in the early disease setting. Window of opportunity trials have been extensively applied to breast cancer. Overall, a wider use of these trial designs might lead to the identification of potential responders, ineffective drugs or combinations, and ultimately contribute to enhance the efficiency of the clinical developmental process.

HER2-HER3 dimer quantification by FLIM-FRET predicts breast cancer metastatic relapse independently of HER2 IHC status

Overexpression of HER2 is an important prognostic marker, and the only predictive biomarker of response to HER2-targeted therapies in invasive breast cancer. HER2-HER3 dimer has been shown to drive proliferation and tumor progression, and targeting of this dimer with pertuzumab alongside chemotherapy and trastuzumab, has shown significant clinical utility. The purpose of this study was to accurately quantify HER2-HER3 dimerisation in formalin fixed paraffin embedded (FFPE) breast cancer tissue as a novel prognostic biomarker.FFPE tissues were obtained from patients included in the METABRIC (Molecular Taxonomy of Breast Cancer International Consortium) study. HER2-HER3 dimerisation was quantified using an improved fluorescence lifetime imaging microscopy (FLIM) histology-based analysis. Analysis of 131 tissue microarray cores demonstrated that the extent of HER2-HER3 dimer formation as measured by Förster Resonance Energy Transfer (FRET) determined through FLIM predicts the likelihood of metastatic relapse up to 10 years after surgery (hazard ratio 3.91 (1.61-9.5), p = 0.003) independently of HER2 expression, in a multivariate model. Interestingly there was no correlation between the level of HER2 protein expressed and HER2-HER3 heterodimer formation. We used a mathematical model that takes into account the complex interactions in a network of all four HER proteins to explain this counterintuitive finding.Future utility of this technique may highlight a group of patients who do not overexpress HER2 protein but are nevertheless dependent on the HER2-HER3 heterodimer as driver of proliferation. This assay could, if validated in a group of patients treated with, for instance pertuzumab, be used as a predictive biomarker to predict for response to such targeted therapies.

HER Targeting in HER2-Negative Breast Cancers: Looking for the HER3 Positive

Targeting HER2 for the treatment of HER2-positive breast cancers is now a validated treatment paradigm. However, evidence suggests that this family of receptors may have important roles outside of the realm of HER2 amplification. There is considerable interest in the development of biomarkers to identify such breast cancers.